## Nombre de participants à l'expérimentation :  58
## Nombre de participants se déclarant comme joueurs :  29
## Nombre de femmes se déclarant comme joueuses :  3
## Age médian des joueurs :  15

Removing Outliers based on BET

(pas nécessaire pour la mesure basée sur l’échelle de confiance)

{r removing.outliers.setup.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SETUP # #------------------------------------------------------ # # DTM <- DTAll[which(DTAll$nom_du_jeu=="Motrice"),] # DTL <- DTAll[which(DTAll$nom_du_jeu=="Logique2"),] # DTS <- DTAll[which(DTAll$nom_du_jeu=="Sensoriel"),] # # # get.outliers <- function(DTDescMLoc,DTDescSLoc,DTDescLLoc){ # outliersM <- boxplot.stats(DTDescMLoc$var)$out # outliersS <- boxplot.stats(DTDescSLoc$var)$out # outliersL <- boxplot.stats(DTDescLLoc$var)$out # # outliers = data.table(type=character(0),id=character(0)) # setkey(outliers,id) # if(length(outliersM) > 0) # outliers = merge(outliers,data.table(id=DTDescMLoc[var %in% outliersM]$IDjoueur,type="Moteur"),by=c("id","type"),all=TRUE) # if(length(outliersS) > 0) # outliers = merge(outliers,data.table(id=DTDescSLoc[var %in% outliersS]$IDjoueur,type="Sensoriel"),by=c("id","type"),all=TRUE) # if(length(outliersL) > 0) # outliers = merge(outliers,data.table(id=DTDescLLoc[var %in% outliersL]$IDjoueur,type="Logique"),by=c("id","type"),all=TRUE) # # return(outliers) # } # # plot.outliers <- function(DT,title){ # p <- ggplot(DT, # aes(type,var)) + # xlab("Difficulty Type") + # ylab(title) # p <- p + geom_boxplot() + geom_point(shape=1) # print(p) # } #

{r detect.outliers.bet.sd, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS BET STD DEV # #------------------------------------------------------ # DTDescM = DTM[,.(type="Moteur",var=sd(miseNorm)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=sd(miseNorm)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=sd(miseNorm)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Bet Standard Dev"); # # outliers = get.outliers(DTDescM,DTDescS,DTDescL) # print(paste("Outliers BET STANDARD DEVIATION:",toString(outliers$id))) # # DTM[IDjoueur %in% unlist(outliers[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliers[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Bet Sd Logical Task");NULL},by=.(IDjoueur)] #

{r detect.outliers.win.sum.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUM OF WINS # #------------------------------------------------------ # # Difficulty : win sum # # # DTDescM = DTM[,.(type="Moteur",var=sum(gagnant)),by=IDjoueur] # # DTDescS = DTS[,.(type="Sensoriel",var=sum(gagnant)),by=IDjoueur] # # DTDescL = DTL[,.(type="Logique",var=sum(gagnant)),by=IDjoueur] # # # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win Sum"); # # # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # # print(paste("Outliers :",toString(outliersLoc$id))) # # # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Motor Task");NULL},by=.(IDjoueur)] # # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Sensory Task");NULL},by=.(IDjoueur)] # # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Win Sum Logical Task");NULL},by=.(IDjoueur)] # #

{r detect.outliers.sheeps.saved.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SAVED SHEEPS # #------------------------------------------------------ # # Difficulty and strategy = saved sheeps # DTDescM = DTM[,.(type="Moteur",var=max(moutons_sauves)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=max(moutons_sauves)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=max(moutons_sauves)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Saved sheeps"); # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # print(paste("Outliers BET SAVED SHEEPS:",toString(outliersLoc$id))) # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Score Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Score Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Score Logical Task");NULL},by=.(IDjoueur)] # #

{r detect.outliers.dda.exploit.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS EXPLOIT DDA # #------------------------------------------------------ # # DDA Exploit : Win/Fail delta sum max # DTDescM = DTM[,.(type="Moteur",var=max(cumulDeltaMise)),by=IDjoueur] # DTDescS = DTS[,.(type="Sensoriel",var=max(cumulDeltaMise)),by=IDjoueur] # DTDescL = DTL[,.(type="Logique",var=max(cumulDeltaMise)),by=IDjoueur] # # plot.outliers(rbind(DTDescM,rbind(DTDescL,DTDescS)), "Win/Fail delta sum max"); # # outliersLoc = get.outliers(DTDescM,DTDescS,DTDescL) # outliers = merge(outliers,outliersLoc,by=c("id","type"),all=TRUE) # print(paste("Outliers BET EXPLOIT DDA:",toString(outliersLoc$id))) # # DTM[IDjoueur %in% unlist(outliersLoc[type=="Moteur"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Motor Task");NULL},by=.(IDjoueur)] # DTS[IDjoueur %in% unlist(outliersLoc[type=="Sensoriel"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Sensory Task");NULL},by=.(IDjoueur)] # DTL[IDjoueur %in% unlist(outliersLoc[type=="Logique"]$id) ,{plot.diff.curve(.SD,"Outlier Delta Bet Logical Task");NULL},by=.(IDjoueur)] #

{r detect.outliers.summary.bet, echo=FALSE} # #------------------------------------------------------ # # OUTLIERS SUMMARY # #------------------------------------------------------ # print(paste("Total number of outliers: ",toString(nrow(unique(outliers,by="id"))))) # print(paste("Total number of outliers motor task: ",toString(nrow(unique(outliers[type=="Moteur"],by="id"))))) # print(paste("Total number of outliers perceptive task: ",toString(nrow(unique(outliers[type=="Logique"],by="id"))))) # print(paste("Total number of outliers logical task: ",toString(nrow(unique(outliers[type=="Sensoriel"],by="id"))))) #

{r remove.outliers.bet, echo=FALSE} # #------------------------------------------------------ # # REMOVING OUTLIERS FROM TABLES # #------------------------------------------------------ # # removing all outliers # DTM <- DTM[!IDjoueur %in% unlist(outliers[type=="Moteur"]$id)] # DTS <- DTS[!IDjoueur %in% unlist(outliers[type=="Sensoriel"]$id)] # DTL <- DTL[!IDjoueur %in% unlist(outliers[type=="Logique"]$id)] # DTAll <- data.table() # DTAll <- rbind(DTAll,DTL) # DTAll <- rbind(DTAll,DTM) # DTAll <- rbind(DTAll,DTS) #

Removing Outliers based on CONFIDENCE SCALE

## [1] "Outliers CS STANDARD DEVIATION: 9b3ph38yc, 9b3ph38yc, a6dfu5ljd, a6dfu5ljd, bzrji9dqz, dyg7cga2o, dyg7cga2o, ejodnl05c, kctu3te1y, tmxmxmwhi, zp9bc59o5, zv35u39vc"
## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers CS NULL: 9b3ph38yc, 9b3ph38yc, 9b3ph38yc, a6dfu5ljd, a6dfu5ljd, a6dfu5ljd, bzrji9dqz, bzrji9dqz, dyg7cga2o, dyg7cga2o, dyg7cga2o, e58u3sinl, kctu3te1y, kctu3te1y, m4ye7uz5h, qzh5zi9e8, tmxmxmwhi, tmxmxmwhi, urgv6o806, zp9bc59o5, zp9bc59o5, zv35u39vc"

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers : "
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers CS SAVED SHEEPS: "
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of outliers:  12"
## [1] "Total number of outliers motor task:  11"
## [1] "Total number of outliers perceptive task:  5"
## [1] "Total number of outliers logical task:  6"

Modeling difficulties

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1669.2   1690.0   -830.6   1661.2     1359 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8343 -0.7720  0.3062  0.7571  2.7501 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.4686   0.6846  
## Number of obs: 1363, groups:  IDjoueur, 47
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -0.9982     0.1974  -5.057 4.27e-07 ***
## difficulty    2.8413     0.2301  12.346  < 2e-16 ***
## timeNorm     -0.5530     0.2179  -2.538   0.0112 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.549       
## timeNorm   -0.577 -0.022
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0      1363         0 
## [1] "Player levels from ranef:"
##   (Intercept)      
##  Min.   :-0.96344  
##  1st Qu.:-0.37670  
##  Median :-0.08364  
##  Mean   :-0.00173  
##  3rd Qu.: 0.21652  
##  Max.   : 1.57591  
## [1] "Intercept: -0.998 4.3e-07 ***"
## [1] "Difficulty: 2.84 5.1e-35 ***"
## [1] "Time: -0.553 0.011 *"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.26"
## [1] "Cross Val: 0.68"
## [1] "AIC: 1700"
##          0%         25%         50%         75%        100% 
## -1.57590870 -0.21652213  0.08364306  0.37669604  0.96343672

##          0%         25%         50%         75%        100% 
## -1.57590870 -0.21652213  0.08364306  0.37669604  0.96343672

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1173.9   1195.1   -582.9   1165.9     1504 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.2906 -0.3676  0.1154  0.3469  6.2131 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.7411   0.8609  
## Number of obs: 1508, groups:  IDjoueur, 52
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -3.1668     0.2640 -11.996   <2e-16 ***
## difficulty    8.1536     0.4159  19.606   <2e-16 ***
## timeNorm     -0.4920     0.2782  -1.768    0.077 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.633       
## timeNorm   -0.505 -0.080
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 0.0610209 (tol =
## 0.001, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
## The result is correct only if all data used by the model has not changed since model was fitted.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0610209 (tol = 0.001, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0         0      1508 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.677712  
##  1st Qu.:-0.448501  
##  Median : 0.077197  
##  Mean   :-0.001156  
##  3rd Qu.: 0.407249  
##  Max.   : 1.510666  
## [1] "Intercept: -3.17 3.7e-33 ***"
## [1] "Difficulty: 8.15 1.4e-85 ***"
## [1] "Time: -0.492 0.077 ."
## [1] "R2 fixed: 0.3"
## [1] "R2 mixed: 0.46"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1200"
##          0%         25%         50%         75%        100% 
## -1.51066561 -0.40724859 -0.07719681  0.44850104  1.67771216

##          0%         25%         50%         75%        100% 
## -1.51066561 -0.40724859 -0.07719681  0.44850104  1.67771216

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1444.5   1465.8   -718.2   1436.5     1533 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.0357 -0.4980 -0.1017  0.5004  5.0622 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.57     1.253   
## Number of obs: 1537, groups:  IDjoueur, 53
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.9054     0.2628  -7.251 4.14e-13 ***
## difficulty    5.7562     0.3198  18.001  < 2e-16 ***
## timeNorm     -1.9355     0.2564  -7.550 4.35e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.497       
## timeNorm   -0.376 -0.233
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##      1537         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.8051717  
##  1st Qu.:-0.7513212  
##  Median :-0.2064150  
##  Mean   :-0.0003176  
##  3rd Qu.: 0.7228639  
##  Max.   : 3.1492300  
## [1] "Intercept: -1.91 4.1e-13 ***"
## [1] "Difficulty: 5.76 1.9e-72 ***"
## [1] "Time: -1.94 4.4e-14 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.8"
## [1] "AIC: 1400"
##         0%        25%        50%        75%       100% 
## -3.1492300 -0.7228639  0.2064150  0.7513212  1.8051717

##         0%        25%        50%        75%       100% 
## -3.1492300 -0.7228639  0.2064150  0.7513212  1.8051717

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.37495, p-value = 0.7077
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04294701

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.91744, p-value = 0.3589
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1000199

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.12965, p-value = 0.8968
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.01388433

Playing board games in general and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.99227, p-value = 0.3211
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##    tau 
## 0.1118

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.21922, p-value = 0.8265
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.02354007

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.6523, p-value = 0.5142
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.06919576

Self efficacy and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 23 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.24953, p-value = 0.8029
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03718731
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 24 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.4833, p-value = 0.01302
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.3393258 
## 
## [1] "self.eff.on.level.s 0.34 0.013 *"
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 27 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.46598, p-value = 0.6412
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.06648267

Risk aversion and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3418, p-value = 0.1797
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1465938

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.9118, p-value = 0.0559
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1966642 
## 
## [1] "risk.av.on.level.s 0.2 0.056 ."

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3781, p-value = 0.1682
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1404273

Age and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.3062, p-value = 0.1915
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1372263
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.9837, p-value = 0.04728
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1984774 
## 
## [1] "age.on.level.s 0.2 0.047 *"
## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.1451, p-value = 0.2522
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1130316

Sex and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.0369, p-value = 0.04166
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2478106 
## 
## [1] "sexe.on.level.m -0.25 0.042 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.068275, p-value = 0.9456
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##          tau 
## -0.007880754

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.38949, p-value = 0.6969
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.04451521

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 163, p-value = 0.04192
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.73654416 -0.04033621
## sample estimates:
## difference in location 
##             -0.3800085 
## 
## [1] "sexe.on.level.m.2 -0.38 0.042 * mean(A): 0.15 mean(B): -0.27"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 294, p-value = 0.9538
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.4708587  0.5066973
## sample estimates:
## difference in location 
##            -0.02056307

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 302, p-value = 0.7064
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.7753238  0.5708569
## sample estimates:
## difference in location 
##            -0.06017729

CONFIDENCE SCALE APPROACH

For Bet approach, see the other file.

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.079 44   0.0014 **
##  2:      0.09375          0.120 54 6.5e-05 ***
##  3:      0.15625          0.110 55 0.00029 ***
##  4:      0.21875          0.120 53   1e-04 ***
##  5:      0.28125          0.100 53 0.00018 ***
##  6:      0.34375          0.094 50 0.00011 ***
##  7:      0.40625          0.074 53     0.033 *
##  8:      0.46875          0.011 53     0.63 :(
##  9:      0.53125         -0.014 50      0.6 :(
## 10:      0.59375         -0.058 54   0.0054 **
## 11:      0.65625         -0.078 52 0.00079 ***
## 12:      0.71875         -0.110 54 3.5e-05 ***
## 13:      0.78125         -0.160 53 3.2e-07 ***
## 14:      0.84375         -0.220 52 1.2e-08 ***
## 15:      0.90625         -0.230 55 3.8e-10 ***
## 16:      0.96875         -0.170 55 1.3e-09 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 44   0.0014 **
##  2: 54 6.5e-05 ***
##  3: 55 0.00029 ***
##  4: 53   1e-04 ***
##  5: 53 0.00018 ***
##  6: 50 0.00011 ***
##  7: 53     0.033 *
##  8: 53     0.63 :(
##  9: 50      0.6 :(
## 10: 54   0.0054 **
## 11: 52 0.00079 ***
## 12: 54 3.5e-05 ***
## 13: 53 3.2e-07 ***
## 14: 52 1.2e-08 ***
## 15: 55 3.8e-10 ***
## 16: 55 1.3e-09 ***
## [1] 52.5
## [1] 2.73

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0520 32     0.071 .
##  2:      0.09375         0.0560 33     0.067 .
##  3:      0.15625         0.0600 37     0.12 :(
##  4:      0.21875         0.0900 35     0.017 *
##  5:      0.28125         0.0770 34     0.026 *
##  6:      0.34375         0.0980 32    0.008 **
##  7:      0.40625         0.0940 35     0.016 *
##  8:      0.46875         0.0150 34     0.64 :(
##  9:      0.53125         0.0036 32     0.93 :(
## 10:      0.59375        -0.0600 38     0.074 .
## 11:      0.65625        -0.1100 32   0.0085 **
## 12:      0.71875        -0.1800 34 9.2e-05 ***
## 13:      0.78125        -0.1700 35 0.00056 ***
## 14:      0.84375        -0.2400 25 0.00024 ***
## 15:      0.90625        -0.2600 26 3.8e-05 ***
## 16:      0.96875        -0.1100 17     0.018 *
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 32     0.071 .
##  2: 33     0.067 .
##  3: 37     0.12 :(
##  4: 35     0.017 *
##  5: 34     0.026 *
##  6: 32    0.008 **
##  7: 35     0.016 *
##  8: 34     0.64 :(
##  9: 32     0.93 :(
## 10: 38     0.074 .
## 11: 32   0.0085 **
## 12: 34 9.2e-05 ***
## 13: 35 0.00056 ***
## 14: 25 0.00024 ***
## 15: 26 3.8e-05 ***
## 16: 17     0.018 *
## [1] 31.9
## [1] 5.23

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.120 27   0.0012 **
##  2:      0.09375          0.160 33   0.0011 **
##  3:      0.15625          0.110 32     0.013 *
##  4:      0.21875          0.081 35     0.011 *
##  5:      0.28125          0.120 32      0.04 *
##  6:      0.34375          0.076 32     0.034 *
##  7:      0.40625          0.044 34     0.55 :(
##  8:      0.46875         -0.011 31     0.88 :(
##  9:      0.53125         -0.015 34     0.78 :(
## 10:      0.59375         -0.048 32     0.29 :(
## 11:      0.65625         -0.150 36   0.0011 **
## 12:      0.71875         -0.069 35     0.021 *
## 13:      0.78125         -0.110 35   0.0013 **
## 14:      0.84375         -0.210 34 1.4e-05 ***
## 15:      0.90625         -0.220 31 6.1e-06 ***
## 16:      0.96875         -0.160 32 8.1e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 27   0.0012 **
##  2: 33   0.0011 **
##  3: 32     0.013 *
##  4: 35     0.011 *
##  5: 32      0.04 *
##  6: 32     0.034 *
##  7: 34     0.55 :(
##  8: 31     0.88 :(
##  9: 34     0.78 :(
## 10: 32     0.29 :(
## 11: 36   0.0011 **
## 12: 35     0.021 *
## 13: 35   0.0013 **
## 14: 34 1.4e-05 ***
## 15: 31 6.1e-06 ***
## 16: 32 8.1e-06 ***
## [1] 32.8
## [1] 2.2

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375          0.140  9     0.15 :(
##  3:      0.15625          0.180 12      0.02 *
##  4:      0.21875          0.130 11     0.068 .
##  5:      0.28125          0.220 11     0.066 .
##  6:      0.34375          0.160  9     0.022 *
##  7:      0.40625          0.190 11     0.068 .
##  8:      0.46875          0.081 14     0.066 .
##  9:      0.53125         -0.031 13      0.4 :(
## 10:      0.59375         -0.094 14     0.073 .
## 11:      0.65625          0.044 12     0.36 :(
## 12:      0.71875         -0.094 15     0.082 .
## 13:      0.78125         -0.150 15     0.021 *
## 14:      0.84375         -0.200 17   0.0023 **
## 15:      0.90625         -0.210 18 0.00089 ***
## 16:      0.96875         -0.330 17 0.00031 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  9     0.15 :(
##  2: 12      0.02 *
##  3: 11     0.068 .
##  4: 11     0.066 .
##  5:  9     0.022 *
##  6: 11     0.068 .
##  7: 14     0.066 .
##  8: 13      0.4 :(
##  9: 14     0.073 .
## 10: 12     0.36 :(
## 11: 15     0.082 .
## 12: 15     0.021 *
## 13: 17   0.0023 **
## 14: 18 0.00089 ***
## 15: 17 0.00031 ***
## [1] 13.2
## [1] 2.83
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

Motor task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375        -0.0440  5 0.78 :(
##  3:      0.15625        -0.0730 19 0.13 :(
##  4:      0.21875         0.0190 35 0.65 :(
##  5:      0.28125         0.0350 40 0.36 :(
##  6:      0.34375         0.0900 40 0.018 *
##  7:      0.40625         0.0540 42 0.19 :(
##  8:      0.46875         0.0560 42 0.098 .
##  9:      0.53125         0.0440 43 0.15 :(
## 10:      0.59375        -0.0100 45 0.91 :(
## 11:      0.65625        -0.0560 44 0.041 *
## 12:      0.71875        -0.0440 43 0.076 .
## 13:      0.78125        -0.0810 38 0.032 *
## 14:      0.84375        -0.1400 23 0.023 *
## 15:      0.90625        -0.0063  7 0.44 :(
## 16:      0.96875        -0.2400  4  0.2 :(
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  5 0.78 :(
##  2: 19 0.13 :(
##  3: 35 0.65 :(
##  4: 40 0.36 :(
##  5: 40 0.018 *
##  6: 42 0.19 :(
##  7: 42 0.098 .
##  8: 43 0.15 :(
##  9: 45 0.91 :(
## 10: 44 0.041 *
## 11: 43 0.076 .
## 12: 38 0.032 *
## 13: 23 0.023 *
## 14:  7 0.44 :(
## 15:  4  0.2 :(
## [1] 31.3
## [1] 15.4
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375        -0.0440  5 0.78 :(
##  3:      0.15625        -0.0730 17 0.057 .
##  4:      0.21875        -0.0190 21 0.61 :(
##  5:      0.28125         0.0190 21 0.42 :(
##  6:      0.34375         0.1100 21 0.023 *
##  7:      0.40625         0.0600 20 0.16 :(
##  8:      0.46875         0.1100 20 0.024 *
##  9:      0.53125         0.1000 19 0.067 .
## 10:      0.59375         0.0880 20 0.18 :(
## 11:      0.65625         0.0051 20    1 :(
## 12:      0.71875        -0.0190 17  0.6 :(
## 13:      0.78125        -0.0560 12 0.25 :(
## 14:      0.84375             NA  0      NA
## 15:      0.90625             NA  0      NA
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  5 0.78 :(
##  2: 17 0.057 .
##  3: 21 0.61 :(
##  4: 21 0.42 :(
##  5: 21 0.023 *
##  6: 20 0.16 :(
##  7: 20 0.024 *
##  8: 19 0.067 .
##  9: 20 0.18 :(
## 10: 20    1 :(
## 11: 17  0.6 :(
## 12: 12 0.25 :(
## [1] 17.8
## [1] 4.77
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  0        NA
##  3:      0.15625          0.290  2      1 :(
##  4:      0.21875          0.069 14   0.29 :(
##  5:      0.28125          0.069 19   0.46 :(
##  6:      0.34375          0.076 19   0.32 :(
##  7:      0.40625          0.020 21   0.83 :(
##  8:      0.46875         -0.019 20   0.93 :(
##  9:      0.53125          0.019 20   0.69 :(
## 10:      0.59375         -0.077 20   0.076 .
## 11:      0.65625         -0.160 20 0.0074 **
## 12:      0.71875         -0.056 21   0.088 .
## 13:      0.78125         -0.081 21   0.21 :(
## 14:      0.84375         -0.160 18   0.029 *
## 15:      0.90625         -0.210  2    0.5 :(
## 16:      0.96875             NA  0        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  2      1 :(
##  2: 14   0.29 :(
##  3: 19   0.46 :(
##  4: 19   0.32 :(
##  5: 21   0.83 :(
##  6: 20   0.93 :(
##  7: 20   0.69 :(
##  8: 20   0.076 .
##  9: 20 0.0074 **
## 10: 21   0.088 .
## 11: 21   0.21 :(
## 12: 18   0.029 *
## 13:  2    0.5 :(
## [1] 16.7
## [1] 6.77
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 0      NA
##  6:      0.34375             NA 0      NA
##  7:      0.40625             NA 1      NA
##  8:      0.46875         0.1800 2  0.5 :(
##  9:      0.53125        -0.0310 4 0.58 :(
## 10:      0.59375        -0.0270 5 0.78 :(
## 11:      0.65625        -0.0059 4    1 :(
## 12:      0.71875        -0.0520 5 0.62 :(
## 13:      0.78125        -0.0940 5 0.31 :(
## 14:      0.84375        -0.0440 5 0.59 :(
## 15:      0.90625        -0.0062 5    1 :(
## 16:      0.96875        -0.2400 4  0.2 :(
## [1] "mean and sd of nb players per bin"
##    nb    pval
## 1:  2  0.5 :(
## 2:  4 0.58 :(
## 3:  5 0.78 :(
## 4:  4    1 :(
## 5:  5 0.62 :(
## 6:  5 0.31 :(
## 7:  5 0.59 :(
## 8:  5    1 :(
## 9:  4  0.2 :(
## [1] 4.33
## [1] 1
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         0.0250 39     0.19 :(
##  2:      0.09375         0.0310 49     0.17 :(
##  3:      0.15625         0.0940 48     0.17 :(
##  4:      0.21875         0.0310 36     0.61 :(
##  5:      0.28125         0.0190 35     0.84 :(
##  6:      0.34375        -0.0190 29     0.71 :(
##  7:      0.40625        -0.0062 32      0.9 :(
##  8:      0.46875        -0.1200 32     0.038 *
##  9:      0.53125        -0.1800 29   0.0038 **
## 10:      0.59375        -0.1900 36 0.00056 ***
## 11:      0.65625        -0.1600 34   0.0016 **
## 12:      0.71875        -0.2200 35 7.2e-05 ***
## 13:      0.78125        -0.2600 34 4.7e-06 ***
## 14:      0.84375        -0.2400 41 6.8e-06 ***
## 15:      0.90625        -0.2100 49 3.9e-08 ***
## 16:      0.96875        -0.1000 52 4.3e-07 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 39     0.19 :(
##  2: 49     0.17 :(
##  3: 48     0.17 :(
##  4: 36     0.61 :(
##  5: 35     0.84 :(
##  6: 29     0.71 :(
##  7: 32      0.9 :(
##  8: 32     0.038 *
##  9: 29   0.0038 **
## 10: 36 0.00056 ***
## 11: 34   0.0016 **
## 12: 35 7.2e-05 ***
## 13: 34 4.7e-06 ***
## 14: 41 6.8e-06 ***
## 15: 49 3.9e-08 ***
## 16: 52 4.3e-07 ***
## [1] 38.1
## [1] 7.48

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125        8.4e-05 17      1 :(
##  2:      0.09375       -4.4e-02 16   0.36 :(
##  3:      0.15625        9.4e-02 15   0.75 :(
##  4:      0.21875        6.6e-03  8      1 :(
##  5:      0.28125        1.9e-02 12   0.91 :(
##  6:      0.34375       -1.7e-01 10   0.066 .
##  7:      0.40625       -1.6e-01  9   0.12 :(
##  8:      0.46875       -2.2e-01 13   0.017 *
##  9:      0.53125       -2.8e-01  9   0.057 .
## 10:      0.59375       -3.4e-01 12 0.0082 **
## 11:      0.65625       -3.6e-01 11 0.0038 **
## 12:      0.71875       -4.2e-01 12 0.0023 **
## 13:      0.78125       -2.8e-01 11 0.0086 **
## 14:      0.84375       -3.2e-01 13 0.0095 **
## 15:      0.90625       -2.3e-01 15 0.0028 **
## 16:      0.96875       -1.1e-01 17   0.037 *
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1: 17      1 :(
##  2: 16   0.36 :(
##  3: 15   0.75 :(
##  4:  8      1 :(
##  5: 12   0.91 :(
##  6: 10   0.066 .
##  7:  9   0.12 :(
##  8: 13   0.017 *
##  9:  9   0.057 .
## 10: 12 0.0082 **
## 11: 11 0.0038 **
## 12: 12 0.0023 **
## 13: 11 0.0086 **
## 14: 13 0.0095 **
## 15: 15 0.0028 **
## 16: 17   0.037 *
## [1] 12.5
## [1] 2.85

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        0.05200 22     0.11 :(
##  2:      0.09375        0.04300 25     0.18 :(
##  3:      0.15625       -0.00630 23     0.79 :(
##  4:      0.21875        0.00630 20     0.96 :(
##  5:      0.28125        0.00016 15        1 :(
##  6:      0.34375        0.05600 15     0.38 :(
##  7:      0.40625        0.01900 18     0.66 :(
##  8:      0.46875       -0.04400 15     0.71 :(
##  9:      0.53125       -0.08100 15     0.14 :(
## 10:      0.59375       -0.09400 17     0.18 :(
## 11:      0.65625       -0.16000 18     0.059 .
## 12:      0.71875       -0.12000 15     0.049 *
## 13:      0.78125       -0.18000 18   0.0021 **
## 14:      0.84375       -0.24000 20    0.002 **
## 15:      0.90625       -0.19000 24 0.00017 ***
## 16:      0.96875       -0.06500 25 0.00051 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 22     0.11 :(
##  2: 25     0.18 :(
##  3: 23     0.79 :(
##  4: 20     0.96 :(
##  5: 15        1 :(
##  6: 15     0.38 :(
##  7: 18     0.66 :(
##  8: 15     0.71 :(
##  9: 15     0.14 :(
## 10: 17     0.18 :(
## 11: 18     0.059 .
## 12: 15     0.049 *
## 13: 18   0.0021 **
## 14: 20    0.002 **
## 15: 24 0.00017 ***
## 16: 25 0.00051 ***
## [1] 19.1
## [1] 3.75

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375          0.160  8    0.1 :(
##  3:      0.15625          0.240 10   0.024 *
##  4:      0.21875          0.070  8   0.44 :(
##  5:      0.28125          0.082  8   0.62 :(
##  6:      0.34375          0.048  4   0.38 :(
##  7:      0.40625          0.160  5   0.28 :(
##  8:      0.46875             NA  4        NA
##  9:      0.53125         -0.180  5   0.058 .
## 10:      0.59375         -0.140  7    0.02 *
## 11:      0.65625          0.044  5   0.78 :(
## 12:      0.71875         -0.094  8   0.29 :(
## 13:      0.78125         -0.280  5   0.054 .
## 14:      0.84375         -0.190  8   0.057 .
## 15:      0.90625         -0.290 10   0.011 *
## 16:      0.96875         -0.240 10 0.0059 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  8    0.1 :(
##  2: 10   0.024 *
##  3:  8   0.44 :(
##  4:  8   0.62 :(
##  5:  4   0.38 :(
##  6:  5   0.28 :(
##  7:  5   0.058 .
##  8:  7    0.02 *
##  9:  5   0.78 :(
## 10:  8   0.29 :(
## 11:  5   0.054 .
## 12:  8   0.057 .
## 13: 10   0.011 *
## 14: 10 0.0059 **
## [1] 7.21
## [1] 2.08
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

Logical task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.094 36   0.0044 **
##  2:      0.09375          0.160 41 3.1e-05 ***
##  3:      0.15625          0.170 42 8.4e-05 ***
##  4:      0.21875          0.260 44 3.2e-06 ***
##  5:      0.28125          0.220 36 0.00012 ***
##  6:      0.34375          0.160 40 5.4e-05 ***
##  7:      0.40625          0.094 44   0.0061 **
##  8:      0.46875          0.031 41     0.038 *
##  9:      0.53125         -0.031 38      0.5 :(
## 10:      0.59375         -0.044 42     0.41 :(
## 11:      0.65625         -0.056 40     0.46 :(
## 12:      0.71875         -0.069 39   0.0097 **
## 13:      0.78125         -0.150 44 0.00022 ***
## 14:      0.84375         -0.230 43 2.1e-07 ***
## 15:      0.90625         -0.260 42 4.7e-07 ***
## 16:      0.96875         -0.350 27 6.1e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 36   0.0044 **
##  2: 41 3.1e-05 ***
##  3: 42 8.4e-05 ***
##  4: 44 3.2e-06 ***
##  5: 36 0.00012 ***
##  6: 40 5.4e-05 ***
##  7: 44   0.0061 **
##  8: 41     0.038 *
##  9: 38      0.5 :(
## 10: 42     0.41 :(
## 11: 40     0.46 :(
## 12: 39   0.0097 **
## 13: 44 0.00022 ***
## 14: 43 2.1e-07 ***
## 15: 42 4.7e-07 ***
## 16: 27 6.1e-06 ***
## [1] 39.9
## [1] 4.3

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.050 26     0.071 .
##  2:      0.09375          0.110 26    0.007 **
##  3:      0.15625          0.120 25     0.015 *
##  4:      0.21875          0.210 24   0.0013 **
##  5:      0.28125          0.150 18     0.074 .
##  6:      0.34375          0.160 21     0.046 *
##  7:      0.40625          0.120 22      0.04 *
##  8:      0.46875          0.031 20     0.44 :(
##  9:      0.53125         -0.031 19      0.5 :(
## 10:      0.59375         -0.094 22     0.12 :(
## 11:      0.65625         -0.073 17     0.42 :(
## 12:      0.71875         -0.100 19     0.034 *
## 13:      0.78125         -0.130 22     0.013 *
## 14:      0.84375         -0.240 19 0.00097 ***
## 15:      0.90625         -0.310 16   0.0029 **
## 16:      0.96875             NA  1          NA
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 26     0.071 .
##  2: 26    0.007 **
##  3: 25     0.015 *
##  4: 24   0.0013 **
##  5: 18     0.074 .
##  6: 21     0.046 *
##  7: 22      0.04 *
##  8: 20     0.44 :(
##  9: 19      0.5 :(
## 10: 22     0.12 :(
## 11: 17     0.42 :(
## 12: 19     0.034 *
## 13: 22     0.013 *
## 14: 19 0.00097 ***
## 15: 16   0.0029 **
## [1] 21.1
## [1] 3.17
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.220 10     0.032 *
##  2:      0.09375          0.360 14   0.0019 **
##  3:      0.15625          0.340 14   0.0015 **
##  4:      0.21875          0.280 16   0.0028 **
##  5:      0.28125          0.220 13   0.0014 **
##  6:      0.34375          0.160 13   0.0018 **
##  7:      0.40625          0.094 15     0.37 :(
##  8:      0.46875          0.031 13     0.12 :(
##  9:      0.53125         -0.031 12     0.49 :(
## 10:      0.59375          0.044 13     0.16 :(
## 11:      0.65625         -0.110 15     0.11 :(
## 12:      0.71875         -0.019 14     0.22 :(
## 13:      0.78125         -0.210 15   0.0055 **
## 14:      0.84375         -0.240 16 0.00068 ***
## 15:      0.90625         -0.240 16   0.0014 **
## 16:      0.96875         -0.340 16 0.00052 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 10     0.032 *
##  2: 14   0.0019 **
##  3: 14   0.0015 **
##  4: 16   0.0028 **
##  5: 13   0.0014 **
##  6: 13   0.0018 **
##  7: 15     0.37 :(
##  8: 13     0.12 :(
##  9: 12     0.49 :(
## 10: 13     0.16 :(
## 11: 15     0.11 :(
## 12: 14     0.22 :(
## 13: 15   0.0055 **
## 14: 16 0.00068 ***
## 15: 16   0.0014 **
## 16: 16 0.00052 ***
## [1] 14.1
## [1] 1.69

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  1        NA
##  3:      0.15625             NA  3        NA
##  4:      0.21875          0.270  4   0.12 :(
##  5:      0.28125          0.340  5    0.1 :(
##  6:      0.34375          0.210  6   0.056 .
##  7:      0.40625          0.230  7   0.15 :(
##  8:      0.46875          0.170  8   0.14 :(
##  9:      0.53125          0.019  7    0.8 :(
## 10:      0.59375         -0.077  7   0.35 :(
## 11:      0.65625          0.094  8   0.29 :(
## 12:      0.71875         -0.069  6   0.53 :(
## 13:      0.78125         -0.068  7   0.67 :(
## 14:      0.84375         -0.220  8   0.041 *
## 15:      0.90625         -0.260 10   0.014 *
## 16:      0.96875         -0.370 10 0.0059 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  4   0.12 :(
##  2:  5    0.1 :(
##  3:  6   0.056 .
##  4:  7   0.15 :(
##  5:  8   0.14 :(
##  6:  7    0.8 :(
##  7:  7   0.35 :(
##  8:  8   0.29 :(
##  9:  6   0.53 :(
## 10:  7   0.67 :(
## 11:  8   0.041 *
## 12: 10   0.014 *
## 13: 10 0.0059 **
## [1] 7.15
## [1] 1.72
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.75587  -0.18208   0.01722   0.17996   0.67980  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.07834    0.02339   3.350  0.00083 ***
## timeNorm     0.01356    0.02393   0.567  0.57104    
## obj.diff    -0.19206    0.03147  -6.103 1.35e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05882497)
## 
##     Null deviance: 82.357  on 1362  degrees of freedom
## Residual deviance: 80.002  on 1360  degrees of freedom
## AIC: 11.387
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.81067  -0.18405  -0.03539   0.21809   0.81473  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.05368    0.01828   2.936  0.00338 ** 
## timeNorm     0.05164    0.02428   2.127  0.03362 *  
## obj.diff    -0.29020    0.01884 -15.405  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06914682)
## 
##     Null deviance: 120.86  on 1507  degrees of freedom
## Residual deviance: 104.07  on 1505  degrees of freedom
## AIC: 255.86
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.73430  -0.20594  -0.01949   0.19850   0.71398  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.21759    0.02001   10.88   <2e-16 ***
## timeNorm     0.05914    0.02495    2.37   0.0179 *  
## obj.diff    -0.53045    0.02119  -25.04   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06995631)
## 
##     Null deviance: 156.54  on 1536  degrees of freedom
## Residual deviance: 107.31  on 1534  degrees of freedom
## AIC: 278.57
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff   n    pval
##  1:      1.5      0.5414894     0.5916709 -0.041797918  94 0.16 :(
##  2:      4.5      0.5347518     0.5750233 -0.031880263 141 0.16 :(
##  3:      7.5      0.5085106     0.5313589 -0.018533292 141 0.41 :(
##  4:     10.5      0.5404255     0.5341000  0.017669339 141 0.43 :(
##  5:     13.5      0.5085106     0.5167958 -0.006673180 141 0.77 :(
##  6:     16.5      0.5276596     0.5259445  0.002686940 141  0.9 :(
##  7:     19.5      0.4971631     0.5307814 -0.035626571 141 0.081 .
##  8:     22.5      0.4737589     0.4890926 -0.014471502 141  0.5 :(
##  9:     25.5      0.4758865     0.4723221  0.005367814 141 0.81 :(
## 10:     28.5      0.4574468     0.4526413  0.002528689 141 0.88 :(
##     time   error.diff shapes
##  1:  1.5 -0.041797918     16
##  2:  4.5 -0.031880263     16
##  3:  7.5 -0.018533292     16
##  4: 10.5  0.017669339     16
##  5: 13.5 -0.006673180     16
##  6: 16.5  0.002686940     16
##  7: 19.5 -0.035626571     16
##  8: 22.5 -0.014471502     16
##  9: 25.5  0.005367814     16
## 10: 28.5  0.002528689     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4682692     0.5971846 -0.13701885 104 2.8e-05 ***
##  2:      4.5      0.5076923     0.6238752 -0.09911341 156 1.4e-07 ***
##  3:      7.5      0.4660256     0.5314762 -0.06766846 156   0.0022 **
##  4:     10.5      0.5160256     0.5982780 -0.07895358 156 8.9e-05 ***
##  5:     13.5      0.4679487     0.5755768 -0.09400514 156 2.4e-07 ***
##  6:     16.5      0.4211538     0.5249293 -0.10790217 156 2.1e-06 ***
##  7:     19.5      0.4794872     0.5492939 -0.05610758 156    0.001 **
##  8:     22.5      0.4993590     0.5710919 -0.05957949 156   0.0025 **
##  9:     25.5      0.5474359     0.5949924 -0.03147437 156     0.069 .
## 10:     28.5      0.4993590     0.5713447 -0.06615746 156   0.0014 **
##     time  error.diff shapes
##  1:  1.5 -0.13701885     24
##  2:  4.5 -0.09911341     24
##  3:  7.5 -0.06766846     24
##  4: 10.5 -0.07895358     24
##  5: 13.5 -0.09400514     24
##  6: 16.5 -0.10790217     24
##  7: 19.5 -0.05610758     24
##  8: 22.5 -0.05957949     24
##  9: 25.5 -0.03147437     16
## 10: 28.5 -0.06615746     24

##     time.bin subj.diff.mean obj.diff.mean    error.diff   n        pval
##  1:      1.5      0.4415094     0.6007697 -1.658770e-01 106 3.8e-06 ***
##  2:      4.5      0.5119497     0.6324837 -1.343840e-01 159 4.2e-06 ***
##  3:      7.5      0.5100629     0.5479813 -4.895619e-02 159     0.069 .
##  4:     10.5      0.5220126     0.5177334  2.196993e-03 159     0.93 :(
##  5:     13.5      0.5169811     0.5303606 -2.035258e-02 159     0.43 :(
##  6:     16.5      0.5100629     0.5026471  2.226322e-05 159        1 :(
##  7:     19.5      0.4584906     0.4514766 -3.401739e-03 159     0.87 :(
##  8:     22.5      0.4226415     0.4287566 -1.335901e-02 159      0.6 :(
##  9:     25.5      0.4584906     0.3964332  6.936761e-02 159     0.013 *
## 10:     28.5      0.4446541     0.3652666  6.326623e-02 159     0.012 *
##     time    error.diff shapes
##  1:  1.5 -1.658770e-01     24
##  2:  4.5 -1.343840e-01     24
##  3:  7.5 -4.895619e-02     16
##  4: 10.5  2.196993e-03     16
##  5: 13.5 -2.035258e-02     16
##  6: 16.5  2.226322e-05     16
##  7: 19.5 -3.401739e-03     16
##  8: 22.5 -1.335901e-02     16
##  9: 25.5  6.936761e-02     24
## 10: 28.5  6.326623e-02     24

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.77507  -0.18892  -0.04563   0.23835   0.56199  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.25898    0.03499   7.401 3.77e-13 ***
## timeNorm     0.10685    0.03407   3.137  0.00178 ** 
## obj.diff    -0.58560    0.03530 -16.589  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06412537)
## 
##     Null deviance: 65.914  on 724  degrees of freedom
## Residual deviance: 46.299  on 722  degrees of freedom
## AIC: 70.941
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7610  -0.2198   0.0094   0.2181   0.7586  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.16969    0.01890   8.977   <2e-16 ***
## timeNorm     0.03591    0.02247   1.598     0.11    
## obj.diff    -0.39843    0.02113 -18.853   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07075553)
## 
##     Null deviance: 155.26  on 1826  degrees of freedom
## Residual deviance: 129.06  on 1824  degrees of freedom
## AIC: 350.94
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.78066  -0.17153  -0.03285   0.20522   0.74779  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.09909    0.01663   5.958 3.04e-09 ***
## timeNorm     0.03821    0.02165   1.765   0.0777 .  
## obj.diff    -0.31369    0.02075 -15.118  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06572702)
## 
##     Null deviance: 138.07  on 1855  degrees of freedom
## Residual deviance: 121.79  on 1853  degrees of freedom
## AIC: 219.61
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5600000     0.7780956 -0.22391727 50 7.2e-06 ***
##  2:      4.5      0.5666667     0.7843893 -0.23287009 75   2e-07 ***
##  3:      7.5      0.6200000     0.7854507 -0.18214977 75 7.4e-06 ***
##  4:     10.5      0.6520000     0.7446165 -0.09638441 75     0.011 *
##  5:     13.5      0.6520000     0.7586383 -0.11964999 75 0.00099 ***
##  6:     16.5      0.6173333     0.7249928 -0.11941562 75   0.0022 **
##  7:     19.5      0.6426667     0.7075656 -0.07119700 75     0.029 *
##  8:     22.5      0.6266667     0.7271200 -0.09999538 75     0.012 *
##  9:     25.5      0.6120000     0.6846440 -0.06660751 75     0.11 :(
## 10:     28.5      0.6346667     0.6657753 -0.02169353 75     0.56 :(
##     time  error.diff shapes
##  1:  1.5 -0.22391727     24
##  2:  4.5 -0.23287009     24
##  3:  7.5 -0.18214977     24
##  4: 10.5 -0.09638441     24
##  5: 13.5 -0.11964999     24
##  6: 16.5 -0.11941562     24
##  7: 19.5 -0.07119700     24
##  8: 22.5 -0.09999538     24
##  9: 25.5 -0.06660751     16
## 10: 28.5 -0.02169353     16

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.5150794     0.6189371 -0.106458111 126 0.00044 ***
##  2:      4.5      0.5798942     0.6776920 -0.094630375 189 9.6e-06 ***
##  3:      7.5      0.5195767     0.5252645 -0.012513224 189     0.57 :(
##  4:     10.5      0.5523810     0.5738743 -0.016145673 189     0.45 :(
##  5:     13.5      0.5296296     0.5716864 -0.041794279 189     0.031 *
##  6:     16.5      0.5232804     0.5553508 -0.037051765 189     0.091 .
##  7:     19.5      0.4984127     0.5539123 -0.056002923 189   0.0049 **
##  8:     22.5      0.4968254     0.5281654 -0.039304341 189     0.085 .
##  9:     25.5      0.5439153     0.5315052  0.009606146 189     0.67 :(
## 10:     28.5      0.5084656     0.5060812 -0.006892386 189     0.72 :(
##     time   error.diff shapes
##  1:  1.5 -0.106458111     24
##  2:  4.5 -0.094630375     24
##  3:  7.5 -0.012513224     16
##  4: 10.5 -0.016145673     16
##  5: 13.5 -0.041794279     24
##  6: 16.5 -0.037051765     16
##  7: 19.5 -0.056002923     24
##  8: 22.5 -0.039304341     16
##  9: 25.5  0.009606146     16
## 10: 28.5 -0.006892386     16

##     time.bin subj.diff.mean obj.diff.mean    error.diff   n      pval
##  1:      1.5      0.4179688     0.5040234 -0.0786571251 128 0.0047 **
##  2:      4.5      0.4369792     0.4794519 -0.0425210394 192   0.033 *
##  3:      7.5      0.4208333     0.4519642 -0.0284750656 192   0.15 :(
##  4:     10.5      0.4500000     0.4513051  0.0000955907 192      1 :(
##  5:     13.5      0.4057292     0.4272861 -0.0215126349 192    0.3 :(
##  6:     16.5      0.3958333     0.3991265 -0.0083645826 192   0.71 :(
##  7:     19.5      0.3927083     0.3883227 -0.0046030177 192   0.79 :(
##  8:     22.5      0.3697917     0.3743096 -0.0078604043 192   0.64 :(
##  9:     25.5      0.3994792     0.3679496  0.0271632447 192   0.13 :(
## 10:     28.5      0.3614583     0.3408703  0.0050885344 192   0.77 :(
##     time    error.diff shapes
##  1:  1.5 -0.0786571251     24
##  2:  4.5 -0.0425210394     24
##  3:  7.5 -0.0284750656     16
##  4: 10.5  0.0000955907     16
##  5: 13.5 -0.0215126349     16
##  6: 16.5 -0.0083645826     16
##  7: 19.5 -0.0046030177     16
##  8: 22.5 -0.0078604043     16
##  9: 25.5  0.0271632447     16
## 10: 28.5  0.0050885344     16

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.72641  -0.18104   0.07896   0.17901   0.32506  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.17441    0.11392   1.531   0.1280  
## timeNorm     0.05973    0.06311   0.946   0.3455  
## obj.diff    -0.34358    0.13212  -2.600   0.0103 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.04409487)
## 
##     Null deviance: 6.6352  on 144  degrees of freedom
## Residual deviance: 6.2615  on 142  degrees of freedom
## AIC: -36.144
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean    error.diff  n    pval
##  1:      1.5      0.7000000     0.8422160 -0.1313282577 10 0.084 .
##  2:      4.5      0.7200000     0.8045511 -0.0795853843 15 0.49 :(
##  3:      7.5      0.6933333     0.7637930 -0.0692528767 15 0.25 :(
##  4:     10.5      0.7200000     0.7894410 -0.0625540262 15 0.36 :(
##  5:     13.5      0.7000000     0.8006171 -0.1084499111 15 0.055 .
##  6:     16.5      0.7200000     0.7661172 -0.0140493196 15  0.8 :(
##  7:     19.5      0.7466667     0.7396280  0.0120888681 15  0.8 :(
##  8:     22.5      0.7333333     0.7489324 -0.0006995685 15    1 :(
##  9:     25.5      0.7533333     0.8163298 -0.0314486706 15  0.6 :(
## 10:     28.5      0.6866667     0.7440259 -0.0101905199 15 0.85 :(
##     time    error.diff shapes
##  1:  1.5 -0.1313282577     16
##  2:  4.5 -0.0795853843     16
##  3:  7.5 -0.0692528767     16
##  4: 10.5 -0.0625540262     16
##  5: 13.5 -0.1084499111     16
##  6: 16.5 -0.0140493196     16
##  7: 19.5  0.0120888681     16
##  8: 22.5 -0.0006995685     16
##  9: 25.5 -0.0314486706     16
## 10: 28.5 -0.0101905199     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7008  -0.1756   0.0104   0.2007   0.6764  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.129171   0.042821   3.017  0.00266 ** 
## timeNorm    -0.001606   0.039220  -0.041  0.96736    
## obj.diff    -0.312573   0.058219  -5.369 1.13e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06994394)
## 
##     Null deviance: 44.480  on 608  degrees of freedom
## Residual deviance: 42.386  on 606  degrees of freedom
## AIC: 113.28
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.5214286     0.6226413 -0.094272559 42   0.054 .
##  2:      4.5      0.5476190     0.6192837 -0.065077266 63   0.076 .
##  3:      7.5      0.5222222     0.5488374 -0.021946708 63   0.54 :(
##  4:     10.5      0.5269841     0.5633358 -0.017849848 63   0.63 :(
##  5:     13.5      0.5365079     0.5457213 -0.003702683 63   0.93 :(
##  6:     16.5      0.5285714     0.5497442 -0.024443456 63    0.5 :(
##  7:     19.5      0.4698413     0.5571074 -0.092288817 63 0.0066 **
##  8:     22.5      0.4412698     0.5026156 -0.066421840 63   0.067 .
##  9:     25.5      0.4777778     0.4906858 -0.015672991 63   0.67 :(
## 10:     28.5      0.4777778     0.4965908 -0.024207547 63   0.42 :(
##     time   error.diff shapes
##  1:  1.5 -0.094272559     16
##  2:  4.5 -0.065077266     16
##  3:  7.5 -0.021946708     16
##  4: 10.5 -0.017849848     16
##  5: 13.5 -0.003702683     16
##  6: 16.5 -0.024443456     16
##  7: 19.5 -0.092288817     24
##  8: 22.5 -0.066421840     16
##  9: 25.5 -0.015672991     16
## 10: 28.5 -0.024207547     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.66104  -0.16469  -0.00053   0.17110   0.56752  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003495   0.031165   0.112    0.911
## timeNorm    0.030048   0.032813   0.916    0.360
## obj.diff    0.014011   0.048027   0.292    0.771
## 
## (Dispersion parameter for gaussian family taken to be 0.04854566)
## 
##     Null deviance: 29.460  on 608  degrees of freedom
## Residual deviance: 29.419  on 606  degrees of freedom
## AIC: -109.12
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n    pval
##  1:      1.5      0.5238095     0.5010468  0.029289623 42 0.44 :(
##  2:      4.5      0.4777778     0.4761135  0.006623743 63 0.82 :(
##  3:      7.5      0.4507937     0.4585390 -0.004803129 63  0.9 :(
##  4:     10.5      0.5111111     0.4440686  0.079755086 63 0.014 *
##  5:     13.5      0.4349206     0.4202938  0.019074305 63 0.57 :(
##  6:     16.5      0.4809524     0.4449609  0.036542116 63 0.21 :(
##  7:     19.5      0.4650794     0.4547299  0.003697701 63 0.89 :(
##  8:     22.5      0.4444444     0.4137030  0.029021771 63 0.27 :(
##  9:     25.5      0.4079365     0.3720518  0.034615081 63 0.19 :(
## 10:     28.5      0.3825397     0.3393145  0.035297116 63 0.18 :(
##     time   error.diff shapes
##  1:  1.5  0.029289623     16
##  2:  4.5  0.006623743     16
##  3:  7.5 -0.004803129     16
##  4: 10.5  0.079755086     24
##  5: 13.5  0.019074305     16
##  6: 16.5  0.036542116     16
##  7: 19.5  0.003697701     16
##  8: 22.5  0.029021771     16
##  9: 25.5  0.034615081     16
## 10: 28.5  0.035297116     16

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74330  -0.20419  -0.03214   0.20665   0.62427  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.21074    0.04485   4.699 4.07e-06 ***
## timeNorm     0.05057    0.05281   0.958    0.339    
## obj.diff    -0.51446    0.04476 -11.495  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06277817)
## 
##     Null deviance: 26.452  on 289  degrees of freedom
## Residual deviance: 18.017  on 287  degrees of freedom
## AIC: 25.206
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.5200000     0.6518365 -0.15393554 20    0.07 .
##  2:      4.5      0.5233333     0.6787481 -0.15856818 30 0.0099 **
##  3:      7.5      0.5600000     0.7244106 -0.17547768 30  0.004 **
##  4:     10.5      0.6166667     0.7076965 -0.09632428 30    0.1 :(
##  5:     13.5      0.6300000     0.7376481 -0.09834264 30   0.047 *
##  6:     16.5      0.5033333     0.6323586 -0.17289582 30   0.025 *
##  7:     19.5      0.5666667     0.6717296 -0.14495973 30   0.064 .
##  8:     22.5      0.6766667     0.7251853 -0.04231065 30   0.56 :(
##  9:     25.5      0.5200000     0.6330884 -0.10243048 30   0.088 .
## 10:     28.5      0.5400000     0.6152669 -0.06054258 30   0.32 :(
##     time  error.diff shapes
##  1:  1.5 -0.15393554     16
##  2:  4.5 -0.15856818     24
##  3:  7.5 -0.17547768     24
##  4: 10.5 -0.09632428     16
##  5: 13.5 -0.09834264     24
##  6: 16.5 -0.17289582     24
##  7: 19.5 -0.14495973     16
##  8: 22.5 -0.04231065     16
##  9: 25.5 -0.10243048     16
## 10: 28.5 -0.06054258     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.73906  -0.18345   0.02625   0.17355   0.80187  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.05584    0.02637   2.118   0.0345 *  
## timeNorm     0.05633    0.03465   1.626   0.1045    
## obj.diff    -0.23667    0.02721  -8.699   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06768238)
## 
##     Null deviance: 54.202  on 724  degrees of freedom
## Residual deviance: 48.867  on 722  degrees of freedom
## AIC: 110.08
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.5280000     0.6066168 -0.086792974 50   0.045 *
##  2:      4.5      0.5920000     0.6783086 -0.060722899 75 0.0014 **
##  3:      7.5      0.4840000     0.4910626 -0.019142965 75   0.56 :(
##  4:     10.5      0.5333333     0.6197591 -0.078082680 75   0.014 *
##  5:     13.5      0.4946667     0.5780738 -0.070454812 75 0.0044 **
##  6:     16.5      0.4706667     0.5383262 -0.062513590 75   0.057 .
##  7:     19.5      0.5160000     0.5458392 -0.010005193 75   0.64 :(
##  8:     22.5      0.5066667     0.5583782 -0.041370638 75   0.081 .
##  9:     25.5      0.6066667     0.6123948 -0.002570066 75   0.89 :(
## 10:     28.5      0.5653333     0.5950078 -0.031782736 75   0.18 :(
##     time   error.diff shapes
##  1:  1.5 -0.086792974     24
##  2:  4.5 -0.060722899     24
##  3:  7.5 -0.019142965     16
##  4: 10.5 -0.078082680     24
##  5: 13.5 -0.070454812     24
##  6: 16.5 -0.062513590     16
##  7: 19.5 -0.010005193     16
##  8: 22.5 -0.041370638     16
##  9: 25.5 -0.002570066     16
## 10: 28.5 -0.031782736     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.70803  -0.14336  -0.04869   0.22741   0.79339  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.006508   0.029732   0.219    0.827    
## timeNorm     0.039932   0.041845   0.954    0.340    
## obj.diff    -0.293722   0.031904  -9.206   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06714128)
## 
##     Null deviance: 38.646  on 492  degrees of freedom
## Residual deviance: 32.899  on 490  degrees of freedom
## AIC: 72.493
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.3500000     0.5511656 -0.21685432 34 0.00084 ***
##  2:      4.5      0.3745098     0.5115479 -0.12017012 51 0.00075 ***
##  3:      7.5      0.3843137     0.4774172 -0.07698230 51     0.019 *
##  4:     10.5      0.4313725     0.5023243 -0.07802084 51     0.012 *
##  5:     13.5      0.3333333     0.4765687 -0.14046332 51 0.00023 ***
##  6:     16.5      0.3000000     0.4420343 -0.13435034 51 9.1e-05 ***
##  7:     19.5      0.3745098     0.4823532 -0.08316170 51 0.00086 ***
##  8:     22.5      0.3843137     0.4991454 -0.09891349 51     0.012 *
##  9:     25.5      0.4764706     0.5469911 -0.04107961 51     0.12 :(
## 10:     28.5      0.3784314     0.5107095 -0.11359359 51   5e-04 ***
##     time  error.diff shapes
##  1:  1.5 -0.21685432     24
##  2:  4.5 -0.12017012     24
##  3:  7.5 -0.07698230     24
##  4: 10.5 -0.07802084     24
##  5: 13.5 -0.14046332     24
##  6: 16.5 -0.13435034     24
##  7: 19.5 -0.08316170     24
##  8: 22.5 -0.09891349     24
##  9: 25.5 -0.04107961     16
## 10: 28.5 -0.11359359     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.72111  -0.14638  -0.09293   0.27600   0.48354  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.39506    0.06828   5.786 1.89e-08 ***
## timeNorm     0.14360    0.05739   2.502   0.0129 *  
## obj.diff    -0.80061    0.06610 -12.112  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06790636)
## 
##     Null deviance: 32.175  on 289  degrees of freedom
## Residual deviance: 19.489  on 287  degrees of freedom
## AIC: 47.977
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5300000     0.8722945 -0.35800592 20 1.9e-05 ***
##  2:      4.5      0.5333333     0.8799494 -0.42037484 30 1.6e-07 ***
##  3:      7.5      0.6433333     0.8573196 -0.24520604 30   0.0013 **
##  4:     10.5      0.6533333     0.7591243 -0.11122511 30      0.1 :(
##  5:     13.5      0.6500000     0.7586393 -0.15551063 30     0.088 .
##  6:     16.5      0.6800000     0.7970647 -0.12033241 30     0.067 .
##  7:     19.5      0.6666667     0.7273704 -0.04953337 30     0.23 :(
##  8:     22.5      0.5233333     0.7181484 -0.19921758 30   0.0022 **
##  9:     25.5      0.6333333     0.6703566 -0.02323196 30     0.84 :(
## 10:     28.5      0.7033333     0.6771584  0.01914086 30     0.75 :(
##     time  error.diff shapes
##  1:  1.5 -0.35800592     24
##  2:  4.5 -0.42037484     24
##  3:  7.5 -0.24520604     24
##  4: 10.5 -0.11122511     16
##  5: 13.5 -0.15551063     16
##  6: 16.5 -0.12033241     16
##  7: 19.5 -0.04953337     16
##  8: 22.5 -0.19921758     24
##  9: 25.5 -0.02323196     16
## 10: 28.5  0.01914086     16
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6621  -0.1253  -0.0197   0.1346   0.5523  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.43590    0.03435  12.689   <2e-16 ***
## timeNorm    -0.02641    0.04010  -0.659     0.51    
## obj.diff    -0.76579    0.03613 -21.197   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05809432)
## 
##     Null deviance: 55.788  on 492  degrees of freedom
## Residual deviance: 28.466  on 490  degrees of freedom
## AIC: 1.1403
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.4882353     0.6324795 -0.143920764 34   0.022 *
##  2:      4.5      0.6019608     0.7489367 -0.164740277 51 0.0021 **
##  3:      7.5      0.5686275     0.5464421  0.006702857 51   0.89 :(
##  4:     10.5      0.6117647     0.5194147  0.088569406 51   0.11 :(
##  5:     13.5      0.5725490     0.5943676 -0.031921449 51   0.49 :(
##  6:     16.5      0.5941176     0.5873127 -0.008698008 51   0.82 :(
##  7:     19.5      0.5078431     0.5618376 -0.061219558 51   0.18 :(
##  8:     22.5      0.5509804     0.5152963  0.034648056 51   0.55 :(
##  9:     25.5      0.5333333     0.4629739  0.074885277 51   0.13 :(
## 10:     28.5      0.4627451     0.3870300  0.082569412 51   0.13 :(
##     time   error.diff shapes
##  1:  1.5 -0.143920764     24
##  2:  4.5 -0.164740277     24
##  3:  7.5  0.006702857     16
##  4: 10.5  0.088569406     16
##  5: 13.5 -0.031921449     16
##  6: 16.5 -0.008698008     16
##  7: 19.5 -0.061219558     16
##  8: 22.5  0.034648056     16
##  9: 25.5  0.074885277     16
## 10: 28.5  0.082569412     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.65879  -0.19764  -0.04055   0.21062   0.72428  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.13031    0.02751   4.737 2.59e-06 ***
## timeNorm     0.06201    0.03610   1.718   0.0863 .  
## obj.diff    -0.38121    0.03503 -10.881  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06928019)
## 
##     Null deviance: 62.177  on 753  degrees of freedom
## Residual deviance: 52.029  on 751  degrees of freedom
## AIC: 131.88
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n    pval
##  1:      1.5      0.3769231     0.4756038 -0.100098761 52 0.041 *
##  2:      4.5      0.4448718     0.4611623 -0.028101324 78 0.43 :(
##  3:      7.5      0.4205128     0.4300114 -0.017591275 78 0.61 :(
##  4:     10.5      0.4128205     0.4237913 -0.009060319 78  0.8 :(
##  5:     13.5      0.4294872     0.4007105  0.033286086 78 0.46 :(
##  6:     16.5      0.3897436     0.3340513  0.051497071 78 0.13 :(
##  7:     19.5      0.3461538     0.2732045  0.067072182 78 0.075 .
##  8:     22.5      0.3000000     0.2608684  0.012950063 78  0.6 :(
##  9:     25.5      0.3423077     0.2475707  0.092260597 78 0.012 *
## 10:     28.5      0.3333333     0.2310782  0.075326505 78 0.051 .
##     time   error.diff shapes
##  1:  1.5 -0.100098761     24
##  2:  4.5 -0.028101324     16
##  3:  7.5 -0.017591275     16
##  4: 10.5 -0.009060319     16
##  5: 13.5  0.033286086     16
##  6: 16.5  0.051497071     16
##  7: 19.5  0.067072182     16
##  8: 22.5  0.012950063     16
##  9: 25.5  0.092260597     24
## 10: 28.5  0.075326505     16

{r plot.subjective.objective.difficulty.confidence.scale, echo=FALSE} # #-------------------------------------------------------------------------------------- # # SHOWING SUBJECTIVE VS OBJECTIVE DIFFICULTY (CONFIDENCE SCALE APPROACH) # #-------------------------------------------------------------------------------------- # # plot.subjective.difficulty <- function(DT,selGroup,title){ # # print(selGroup) # # # Lien entre mise normalisée et difficultée estimée (hard / easy effect) # obj.diff.quants = seq(0,1,1/16)#quantile(DT$obj.diff, probs=(seq(0,1,0.05))) # nb.bins = length(obj.diff.quants)-1 # subj.diff.med = numeric(nb.bins) # obj.diff.bin = numeric(nb.bins) # obj.diff.bin.cur = 0; # test.pvals = numeric(nb.bins) # conf.min = numeric(nb.bins) # conf.max = numeric(nb.bins) # nb.vals = numeric(nb.bins) # shapes = numeric(nb.bins) # delta.obj.subj = numeric(nb.bins) # hist(DT$obj.diff) # for(i in 1:nb.bins){ # #obj.diff.bin.cur = round(i/10,1) # #subj.diff = DT[round(obj.diff,1)==obj.diff.bin.cur]$subj.diff.mise # obj.diff.bin.cur = (obj.diff.quants[i] + obj.diff.quants[i+1])/2.0 # #subj.diff = DT[obj.diff > obj.diff.quants[i] & obj.diff<=obj.diff.quants[i+1]]$subj.diff.mise # DTLoc = DT[obj.diff > obj.diff.quants[i] & obj.diff<=obj.diff.quants[i+1]] # if(selGroup != "all") # DTLoc = DTLoc[niveau.group==selGroup] # DTLoc = DTLoc[,.(confiance.mean=mean(subj.diff.confiance)),by=IDjoueur] # subj.diff = DTLoc$confiance.mean # obj.diff.bin[i] = obj.diff.bin.cur # subj.diff.med[i] = NA # test.pvals[i] = NA # conf.min[i] = NA # conf.max[i] = NA # delta.obj.subj[i] = NA # shapes[i] = 16 # nb.vals[i] = length(subj.diff) # if(nb.vals[i] > 1){ # try.res = try(test.res <- wilcox.test(subj.diff,mu = obj.diff.bin.cur,conf.int=T)) # if (class(try.res) != "try-error"){ # #print(test.res) # #hist(subj.diff) # test.pvals[i] = format.pval.stars(test.res$p.value) # if(test.res$p.value < 0.05) # shapes[i] = 24 # #subj.diff.med[i] = mean(subj.diff) # subj.diff.med[i] = test.res$estimate # conf.min[i] = test.res$conf.int[1] # conf.max[i] = test.res$conf.int[2] # delta.obj.subj[i] = signif(subj.diff.med[i] - obj.diff.bin.cur,digit=2) # } # } # } # # #print table of pvalues # print(data.table(obj.diff.bin=obj.diff.bin,delta.obj.subj=delta.obj.subj,n=nb.vals,pval=test.pvals)) # # #summary # print("mean and sd of nb players per bin") # DTNbVals = data.table(nb = nb.vals, pval=test.pvals) # print(DTNbVals[!is.na(pval)]) # print(signif(mean(DTNbVals[!is.na(pval)]$nb),digits=3)) # print(signif(sd(DTNbVals[!is.na(pval)]$nb),digits=3)) # # #kernel smooth # subj.diff.smooth <- ksmooth(x=DT$obj.diff,y=DT$subj.diff.confiance,bandwidth = 0.2) # DTSmooth = data.table(x=subj.diff.smooth$x,y=subj.diff.smooth$y) # # DTPlot = data.table(obj.diff=obj.diff.bin,subj.diff=subj.diff.med, shapes=shapes) # # p = ggplot() + ggtitle(title) + # # geom_line(aes(x=DTPouet$x,y=DTPouet$y))+ # geom_point(aes(x=DTPlot$obj.diff,y=DTPlot$subj.diff),alpha = 1, size = 3, shape=DTPlot$shapes) + # xlim(0,1)+ # ylim(0,1)+ # geom_errorbar(aes(x=DTPlot$obj.diff, ymin=conf.min, ymax=conf.max), width=.01,color="red") + # geom_abline(intercept = 0, slope = 1, color="blue") + # xlab("Objective Difficulty") + ylab("Subjective Difficulty") + theme(text = element_text(size=15)) # # print(p) # } #

All tasks

{r plot.subjective.difficulty.all.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTAll,"all", "All tasks, all groups") # plot.subjective.difficulty(DTAll,"good", "All tasks, good") # plot.subjective.difficulty(DTAll,"medium", "All tasks, medium") # plot.subjective.difficulty(DTAll,"bad", "All tasks, bad") #

Motor task

{r plot.subjective.difficulty.motor.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTM,"all", "Motor, all") # plot.subjective.difficulty(DTM,"good", "Motor, good") # plot.subjective.difficulty(DTM,"medium", "Motor, medium") # plot.subjective.difficulty(DTM,"bad", "Motor, bad") #

Sensory task

{r plot.subjective.difficulty.sensory.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTS,"all","Sensory, all") # plot.subjective.difficulty(DTS,"good","Sensory, good") # plot.subjective.difficulty(DTS,"medium","Sensory, medium") # plot.subjective.difficulty(DTS,"bad","Sensory, bad") #

Logical task

{r plot.subjective.difficulty.logical.confidence.scale, echo=FALSE} # plot.subjective.difficulty(DTL,"all","Logical, all") # plot.subjective.difficulty(DTL,"good","Logical, good") # plot.subjective.difficulty(DTL,"medium","Logical, medium") # plot.subjective.difficulty(DTL,"bad","Logical, bad") #